import pandas as pd
from mlxtend.preprocessing import TransactionEncoder
from mlxtend.frequent_patterns import apriori, association_rules

df = pd.read_excel('./associationRules/餐厅数据.xlsx')

print(df.head)


trsations = df['菜品'].str.split(',').to_list()

ts = TransactionEncoder()
te_ary = ts.fit(trsations).transform(trsations)
df_enecoded = pd.DataFrame(te_ary, columns=ts.columns_)

#print(df_enecoded.head)


frequent_itemsets = apriori(df_enecoded, min_support=0.1, use_colnames=True)
frequent_itemsets.sort_values(by='support', ascending=False, inplace=True)

#print(frequent_itemsets[frequent_itemsets.itemsets.apply(lambda x : len(x) == 2)])

rules = association_rules(frequent_itemsets, metric='confidence',min_threshold=0.1)

effective = rules[
    (rules['lift']> 1) & (rules['confidence'] > 0.1)
].sort_values(by=['lift','confidence'], ascending=False)

#生成相应的模型
frequent_itemsets.to_pickle("frequent_itemsets.pkl")
effective.to_pickle('rule.pkl')